Methylenetetrahydrofolate Reductase (MTHFR): A Novel Target for Cancer Therapy
Bibliographic record
Abstract
Tumor cells have an enhanced requirement for glucose, amino acids and DNA precursors. Since folates are required for the synthesis of thymidine and purines, the metabolism of folate has been exploited as an anti-cancer target for over 6 decades, with emphasis on the inhibition of DNA synthesis. However, folate is also used to generate methionine, which is essential for proliferation by virtue of its role in protein synthesis, polyamine synthesis and transmethylation reactions. Tumor-derived cell lines and human tumor xenografts have been shown to be methionine dependent i.e., they are unable to survive without methionine and are unable to efficiently utilize homocysteine, the immediate metabolic precursor of methionine. Since non-transformed cells are methionine-independent, the targeting of methionine metabolism presents an opportunity to selectively disrupt the unique metabolic networks in cancer cells. This chapter provides an overview of the critical role of folate and methionine metabolism in tumor cells and summarizes the current anti-folate and anti-methionine strategies to inhibit growth of transformed lines and tumors. We also present our work on the development of a novel anti-cancer target, methylenetetrahydrofolate reductase (MTHFR), a key enzyme of both folate and methionine metabolism. Our data demonstrate that antisense-mediated inhibition of MTHFR is associated with increased cytotoxicity in vitro and with decreased growth of tumors in vivo. These findings warrant further investigation of this enzyme and the methionine biosynthetic pathway in exploring new strategies for cancer chemotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".